Recent publications
Human skin is a multilayered nanocomposite that serves as a protective barrier against external factors. Utilizing multilayered skin substitutes that mimic the structure of human skin can considerably enhance the wound-healing process. In this study, different amounts of zinc oxide nanoparticles (n-ZnO) were added to a poly(ε-caprolactone) (PCL) solution, which was poured into molds containing NaCl porogens of two particle sizes. A salt leaching and solvent casting procedure was used to construct PCL/n-ZnO scaffolds with a bilayer structure. These scaffolds were analyzed to determine their phase structure, chemical functional groups, mechanical properties, biodegradation behavior, and cell viability and adhesion characteristics. The results indicated that none of the scaffolds had degraded considerably or lost much mass after 30 days of immersion in phosphate-buffered saline. Compressive mechanical analysis revealed that the PCL/15n-ZnO/CS scaffold exhibited the highest compressive strength and modulus, approximately 2.1 and 3.5 MPa, respectively. However, after 1 week of cell culture, cell viability was adequate for none of the synthesized scaffolds. In addition, the biocompatibility of the scaffolds decreased with increasing n-ZnO amount. Taken together, these findings indicate that although PCL/n-ZnO scaffolds exhibit promising mechanical properties, the concentration of ZnO must be optimized to ensure the biocompatibility of the scaffolds without compromising their strength. Future studies should focus on balancing the mechanical performance and cellular survival of scaffolds by incorporating various nanoparticle concentrations or surface changes and improving the design of scaffolds for more effective biodegradation and tissue regeneration.
This study explores the advantages of utilizing the rough surfaces of additively manufactured implants. For this purpose, samples of Ti6Al4V, fabricated through laser powder-bed fusion (LPBF), were anodized before and after surface polishing. The roughness of the as-built samples was 4.34 Ra, while the polished samples had a roughness of 0.037 Ra. This resulted in the formation of amorphous TiO2 nanotubes. The nanotubes grown on the rough surface exhibited inner and outer diameters of 65 and 143 nm, respectively, which was 35% larger in diameter and 24% thicker compared to those formed on smooth surfaces. The PCL composite coating composed of Ag-doped bioactive glass electrosprayed on the surfaces showed that rough surfaces had a higher coating capacity. Also, the coating adhered to the rough surface with an approximate strength of 5.78 MPa, compared to 2.67 MPa on the smooth surface. Interestingly, corrosion resistance of the as-built samples was found to be higher than that of the polished samples. The results indicate that the utilization of anodized additive manufactured sample can have a great potential for facile implant fabrication.
The Ti6Al4V implants were fabricated using laser powder-bed fusion, followed by surface modification techniques to optimize surface properties. A nanocomposite coating composed of polycaprolactone (PCL) and silver-doped bioactive glass nanoparticles was applied through electrospraying. Comprehensive surface characterization was performed using a variety of techniques to evaluate and confirm the coating’s suitability for medical applications
The objective of this work is to create an ABAQUS plugin for predicting the failure mechanism, and mechanical characteristics of weft-knitted reinforced composites utilizing multi-scale modeling. This plugin facilitates the automatic modeling and analysis of weft-knitted reinforced composites, focusing on parameters such as stiffness, strength, and failure mechanisms. The developed plugin estimates the homogenized effective elastic properties of a user-created macro-model for a weft-knitted reinforced composite structure. The plugin correctly extracts the concepts of homogenization based on micromechanics parametric inputs of fiber and resin which are considered separately by the software’s user. Afterward, the homogenized constants of the composites are automatically applied to the macro-model to achieve the most susceptible areas for failure after the localization step. It also enables the prediction of the composite strength and the identification of the sample’s critical mesoscale regions. This paper also explains the plugin’s homogenization and localization-based approach. Prior to carrying out parametric research, the simulation findings are verified using experimental data. Furthermore, experimental instances demonstrating its implementation and validation are provided. A comparative analysis of tensile characteristics between the multi-scale finite element model and experimental results disclosed that the model exhibited an overestimation of the failure strength in the course and wale directions by approximately 13%. Furthermore, the error due to predicting the tensile modulus in both directions is less than 7%. The results obtained from the prediction of the plugin revealed the approximate locations of failures within the composite unit cell under tensile loading in both course and wale directions.
Nowadays, resources of fossil fuels cause growing problems such as environmental pollution and global warming. Therefore, replacing renewable energy sources, such as thermal, nuclear, solar, and mechanical energy, has attracted worldwide attention. Piezoelectric nanogenerators, converting mechanical energy to electrical energy using the piezoelectric effect, are attractive for renewable energy. However, they have some drawbacks, such as low electrical performance, low efficiency, and limited materials to select. Since introducing triboelectric nanogenerators (TENGs) in 2012 by Wang, harvesting mechanical energy from water, wind, and human body motion using TENGs has received more interest due to their unique features, including high electrical output, low weight, and high efficiency. Besides, a wide variety of materials, including polymers, metals, and carbon materials, have been applied in TENGs to enhance their performance. Graphene is a proper selection for TENGs because of its outstanding properties, such as high specific surface area, electrical conductivity, mechanical strength, and flexibility. This paper reviews recent progress in the fabrication, performance, and application of graphene-based TENGs. Moreover, current challenges and perspectives for research work will be highlighted.
Tall fescue (Festuca arundinacea Schreb. syn. Lolium arundinaceum), an important cool‐season grass, has limited understanding regarding its genetic inheritance patterns and the potential for simultaneous selection across forage, seed, and turf traits. In this study, 24 half‐sib families derived from polycrosses, along with their corresponding parental genotypes, were assessed for different agro‐morphological, seed, and turf quality characteristics in the field for 2 years (2019–2020). High genotypic variation was observed for all the measured traits. Moderate narrow‐sense heritability (h²PFM) for turf quality and seed‐related traits indicated that genetic variance predominates in total phenotypic variance of these traits. Low value of h²PFM (0.25) for dry forage yield (DFY) shows the high environmental influence on the expression of this economic trait. Indirect selection to improve DFY was more effective through its components, such as crown diameter (h²PFM = 0.43) and plant height (h²PFM = 0.48), which had higher heritability and positive correlation with forage yield. However, for seed and turf quality traits, direct selection would be possible during recurrent selection programs. The simultaneous selection for both forage yield and seed yield would be possible due to the positive correlation between them. Based on the application of multivariate analysis, parental genotypes and half‐sib families with possible utility as forage and seed use or turf application were recognized, which can be used in the future breeding programs for developing synthetic varieties.
Overgrowth of Phragmites australis has become a serious issue for Zarivar Lakes and many water bodies worldwide. The objective of this research was to use the harvested common reed as a soil organic amendment. For this purpose, the release kinetics of phosphorus and potassium from Phragmites a. biomass were studied. This study was conducted as a split-plot design with two treatments including a control and 3% (w/w) Phragmites a. and eight incubation durations (7, 14, 28, 42, 56, 70, 84, 98 days) in a fully randomized design with three replications. After 98 days, the amount of cumulative phosphorus released increased from 12.4 to 127.3 mg/kg in the control treatment and from 18.9 to 150.1 mg/kg in the 3% (w/w) treatment, while potassium released increased from 269.6 to 2206.1 mg/kg in the control treatment and from 322.2 to 2584.3 mg/kg in the 3% treatment. The release intensity (b) of phosphorus was larger than that of potassium in the exponential equation, and the release of the elements was a diffusion function in the parabolic equation. In the Phragmites a., dominant functional groups (C-H) and (C = O) were reported. The surface of the plant was smooth and dense. The results showed that the Phragmites a. can be a source of phosphorus and potassium and does not cause heavy metal pollution to the soil and exponential and parabolic equations had the highest correlation with the release of elements. Phragmites a. can be used as an environment-friendly organic amendment, however, additional research is needed.
Chlorosis, a significant issue affecting plane trees in urban green spaces, was the focus of this research. The aim was to investigate the effectiveness of a combined strategy involving soil amendment and feeding in reducing chlorosis and enhancing tree health. A factorial study was conducted using a randomized complete block design with four replications on plane trees in an urban setting. The study included two trunk injection (endotherapy) levels (non-injection and injection) and four fertilization techniques (control, dig-hole fertilizer, mulch, and dig-hole fertilizer + mulch), both individually and combined. The study revealed that the injection treatment played a crucial role in facilitating the absorption of essential nutrients such as iron and phosphorus, leading to increased chlorophyll levels and overall improved physical health of the plane trees. Furthermore, the combination of injection and dig-hole fertilizer and also injection and mulch resulted in enhanced iron and zinc concentrations in the leaves. During the summer, the combined application of injection, dig-hole fertilizer, and mulch significantly enhanced tree health, as evidenced by a remarkable 40% increase in total chlorophyll concentration compared to the control group. This holistic approach not only boosted chlorophyll levels but also improved the physical health of the trees over the months from June to September, with enhancements of 55%, 30%, 55%, and 69% respectively, when compared to the control treatment. Overall, the findings emphasize the importance of improving soil conditions for efficient nutrient uptake by trees in urban green spaces. Tree health significantly improved by enriching the soil with additional elements through dig-hole fertilizer and trunk injection methods.
Wastewater from wheat starch industries is the one with high chemical oxygen demand (COD) level that has adverse effects on the environment and thus special attention to its treatment for the discharge limits satisfaction is crucial. Biological treatment methods have challenges such as requiring extensive space and process time, high sludge production, and efficient management and operation demands. To overcome these challenges, electrochemical methods such as electrocoagulation (EC) and electro-Fenton (EF) can be efficient approaches due to their higher process speeds, minimal facility requirements, and easy operation which make them economically viable. In this study, electrochemical processes, including EC and EF methods were applied for wastewater treatment of a wheat starch industry. After preliminary experiments to identify the effective factors and ranges, the response surface method (RSM) was applied to design the experiments. In RSM seven factors were considered including initial COD, pH, electrode distances, process time, temperature, current intensity, and hydrogen peroxide concentration along with the COD removal efficiency as the response. Statistical analysis showed that hydrogen peroxide concentration and initial COD had the most significant impact, while pH had the least effect on COD removal in the electrochemical process. The optimum results showed that for synthetic wastewater with an initial COD range of 2000–4000 mg/L a COD removal of 75–85% for EC and 89–93% removal for EF were obtained. The results were validated for raw natural wastewater with 88% removal for EC and 92% for EF. In conclusion, while the removal efficiency of the EF process was superior to EC, the former incurs higher costs due to the use of hydrogen peroxide.
Over the past two decades, the rise in video streaming has been driven by internet accessibility and the demand for high-quality video. To meet this demand across varying network speeds and devices, transcoding is essential. This paper introduces a parametric rate-distortion (R-D) transcoding model that predicts transcoding distortion at different bitrates without the need for re-encoding. Experimental results validate the model’s effectiveness in predicting rate-distortion behavior for diverse video content. Using our model, visual quality (measured by PSNR and VMAF) of transcoded video can be improved through trans-sizing. Moreover, our model can identify visually lossless bitrate ranges. This allows service providers to adjust target bitrates with minimal quality loss. Experimental results validate the model’s effectiveness in predicting rate-distortion behavior for diverse video content. By using the VMAF measure, our model achieves a quality improvement of up to 2.55 and bitrate savings of up to 79.10%.
This work presents the development of a novel nanocarrier system consisting of graphene oxide (GO), polyvinylpyrrolidone (PVP), and sodium alginate (SA) for precise and regulated delivery of 5-Fluorouracil (5-FU), a commonly used chemotherapy drug for remission of colorectal cancer. In comparison to systems lacking graphene oxide (GO), the nanocomposite demonstrated a substantial increase in drug loading capacity (46%) and entrapment capacity (85%). The particle size of 297 nm was obtained via experimental investigation of dynamic light scattering (DLS). Analysis of zeta potential showed a value of − 39 mV, indicating a very stable colloidal structure. Drug release experiments conducted in vitro showed that the release of 5-FU was sensitive to pH and accurately adjusted to the acidic conditions often seen in tumor microenvironments (pH 5.4). An investigation of cytotoxicity showed that the SA/PVP/GO@5-FU nanocarrier reduced the survival of colorectal cancer (HCT-116) cells by 50% while maintaining 90% survival in normal L929 fibroblast cells. This study indicates that the SA/PVP/GO@5-FU nanocarrier system has potential as a basis for enhancing the therapeutic efficacy of 5-FU by increasing its selectivity, stability, and release rate, thereby reducing systemic toxicity in cancer therapy.
Graphical Abstract
In this study, magnesium aluminate spinel (MgAl2O4) particles were synthesized at 500 °C using the combustion method followed by calcination at 800 °C. Subsequently, PLA (Polylactic acid)/MgAl2O4 scaffolds were fabricated and developed employing the slurry casting approach. In this research, 4 and 8 wt.% of magnesium aluminate spinel were used for composite production. To analyze the crystal structure, surface chemistry, microstructure, and biodegradability of the produced composites, X-ray diffraction analysis (XRD), Fourier transform infrared (FTIR), field scanning electron microscopy (FESEM), inductively coupled plasma (ICP), and potentiometric evaluations were employed. The Rietveld results of XRD data revealed that MgAl2O4 particles have been properly dispersed in the slurry casted specimens. FTIR characterization confirmed bonding formation between the MgAl2O4 reinforcement and the PLA matrix. FESEM/EDX results indicated that MgAl2O4 spinel, with the sub-micron-sized particles, significantly accelerated the degradation rate. As the ICP analysis confirmed the bioactivity of the PLA/MgAl2O4 composites by observing a meaningful reduction of Ca and P elements in simulated body fluid (SBF), it is hopefully concluded that the mentioned composites can be employed as effective biomaterials. Moreover, an increase in pH after 4 weeks indicated the degradation of composites. Meanwhile, the values of pH vary between 7.6 and 7.8, which is close to the pH of the human body.
Background
Computer-aided diagnosis (CAD) methods have become of great interest for diagnosing macular diseases over the past few decades. Artificial intelligence (AI)-based CADs offer several benefits, including speed, objectivity, and thoroughness. They are utilized as an assistance system in various ways, such as highlighting relevant disease indicators to doctors, providing diagnosis suggestions, and presenting similar past cases for comparison.
Methods
Much specifically, retinal AI-CADs have been developed to assist ophthalmologists in analyzing optical coherence tomography (OCT) images and making retinal diagnostics simpler and more accurate than before. Retinal AI-CAD technology could provide a new insight for the health care of humans who do not have access to a specialist doctor. AI-based classification methods are critical tools in developing improved retinal AI-CAD technology. The Isfahan AI-2023 challenge has organized a competition to provide objective formal evaluations of alternative tools in this area. In this study, we describe the challenge and those methods that had the most successful algorithms.
Results
A dataset of OCT images, acquired from normal subjects, patients with diabetic macular edema, and patients with other macular disorders, was provided in a documented format. The dataset, including the labeled training set and unlabeled test set, was made accessible to the participants. The aim of this challenge was to maximize the performance measures for the test labels. Researchers tested their algorithms and competed for the best classification results.
Conclusions
The competition is organized to evaluate the current AI-based classification methods in macular pathology detection. We received several submissions to our posted datasets that indicate the growing interest in AI-CAD technology. The results demonstrated that deep learning-based methods can learn essential features of pathologic images, but much care has to be taken in choosing and adapting appropriate models for imbalanced small datasets.
Background
The pharmaceutical industry has seen increased drug production by different manufacturers. Failure to recognize future needs has caused improper production and distribution of drugs throughout the supply chain of this industry. Forecasting demand is one of the basic requirements to overcome these challenges. Forecasting the demand helps the drug to be well estimated and produced at a certain time.
Methods
Artificial intelligence (AI) technologies are suitable methods for forecasting demand. The more accurate this forecast is the better it will be to decide on the management of drug production and distribution. Isfahan AI competitions-2023 have organized a challenge to provide models for accurately predicting drug demand. In this article, we introduce this challenge and describe the proposed approaches that led to the most successful results.
Results
A dataset of drug sales was collected in 12 pharmacies of Hamadan University of Medical Sciences. This dataset contains 8 features, including sales amount and date of purchase. Competitors compete based on this dataset to accurately forecast the volume of demand. The purpose of this challenge is to provide a model with a minimum error rate while addressing some qualitative scientific metrics.
Conclusions
In this competition, methods based on AI were investigated. The results showed that machine learning methods are particularly useful in drug demand forecasting. Furthermore, changing the dimensions of the data features by adding the geographic features helps increase the accuracy of models.
A power DC‐DC Buck‐Boost converter is controlled using a Lyapunov‐based Adaptive Backstepping Control (ABSC) technique. It exhibits unfavorable behavior due to its non‐minimum structure, necessitating a well‐regulated controller to guarantee stability. This strategy is an enhanced iteration of the technique that uses the stability Lyapunov function to achieve greater stability and improved resistance to disturbances in real‐world scenarios. Furthermore, the Black‐box technique is employed to minimize the computing workload and facilitate implementation, under the assumption that there is no precise mathematical model available for the system. However, in real‐time settings, disruptions with broader scopes such as fluctuations in supply voltage, variations in parameters, and noise might have adverse effects on the functioning of this approach. There is a need to set the most suitable initial gains for the controller to enhance its flexibility in more challenging working conditions. Therefore, to meet this requirement and enhance the effectiveness of the controller, the control scheme integrates a computational method called the Snake optimization (SO) algorithm. The SO method is known for its disciplined and nature‐inspired approach, which results in faster decision‐making and greater accuracy compared to other optimization algorithms. In order to further explain the advantages of this method, classical Backstepping and SO‐based PID schemes are also developed and evaluated in various scenarios. The effectiveness of this approach is tested in both simulation and experimental environments, showing significant outcomes and lower sensitivity to error.
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